Data Visualization report on Bike Ride Trends and Biker Types of Ford GoBike System¶

by Mohammad Hanafy¶

Investigation Overview¶

In this investigation, I wanted to shed the light on the characteristics of trip data that could be used to predict their duration. The main focus was on the : age, distance user type, and gender.

Dataset Overview¶

The data set includes information about individual rides made in a bike-sharing system covering the greater San Francisco Bay area, the data set contains approximately 183,412 records and 16 feature.

Note that the above cells have been set as "Skip"-type slides. That means that when the notebook is rendered as http slides, those cells won't show up.

Distribution of Trips' Durations¶

Trip Durations in the dataset take on a very large range of values. Number of Trips values first increases starting from around 1400 values to 12500 values at peak around 350 seconds but then starts to fall below at 2000 values.

Distribution of User Age¶

The age values are condensed between 20 and 40 years.

Trip Duration vs Age¶

The chart below shows that the most frequent users aged between 20 and 45

Trip Duration, Age and Gender¶

The main thing I want to explore in this part of the analysis is how the three categorical measures of gender into the relationship between trip duration and age.

Trip Duration, Age and User Type¶

For the age, duration, and user type, both Customer and Subscriber are showing similar trends for age and trip duration, but for subscribers the trip duration is higher for older age.